Hyperspectral Image Classification via Joint Sparse Representation

Abstract

Many researches have been proved that the most important information of high-dimensional data (e.g. hyperspectral images, HSI) lies in a low-dimensional subspace spanned by some specific bases. Moreover, image pixels belonging to the same class are usually distributed in the same low-dimensional subspace. Therefore, the reduction of dimensionality for classification has become the major issue in hyperspectral image analysis. Sparse representation techniques can be used to process data with sparsity, so it is quite suitable for hyperspectral image analysis. Based on the sparse representation, the paper proposes a method suitable for hyperspectral image classification with learned dictionaries. In the proposed method, the spectral and spatial information are integrated simultaneously into a joint sparse representation in order to increase performances of hyperspectral image classification.